
Deep metric learning tries to learn an embedding space, where closeness between embeddings encodes the level of semantic similarities between the data samples. This is done by leveraging deep neural networks to learn the mapping between the data samples and the embedding space and enforcing the embeddings belonging to the same class to lie close while pushing those belonging to different classes further apart. Deep metric learning-based methods have achieved state-of-the-art (SOTA) results for several tasks, like face recognition [1]}, [2]}, [3]}, [4]}, re-identification [5]}, [6]}, [7]}, [8]}, image retrieval [9]}, [10]}, etc.
<FIGURE>